Few-shot classification through reconstruction:
Feature reconstruction is a classic approach [1]} to object tracking and alignment [2]}, [3]}, [4]}, [5]}, but has only recently been utilized for few-shot classification.
DeepEMD [6]} formulates reconstruction as an optimal transport problem.
This formulation is sophisticated and powerful, but training and inference come with significant computational cost, due to the reliance on iterative constrained convex optimization solvers and test-time SGD.
CrossTransformer [7]} and CrossAttention [8]} add attention modules that project query features into the space of support features (or vice versa), and compare the class-conditioned projections to the target to predict class membership.
These attention-based approaches introduce many additional learned parameters over and above the network backbone, and place substantial constraints on the projection matrix (weights are non-negative and rows must sum to 1).
In contrast, FRN efficiently calculates minimally constrained, least-squares-optimal reconstructions in closed form.
